Search

Multifactorial diseases demand therapeutics that can modulate multiple targets for enhanced safety and efficacy, yet the clinical approval of multitarget drugs remains rare. The integration of machine learning (ML) and deep learning (DL) in drug discovery has revolutionized virtual screening. This study investigates the synergy between ML/DL methodologies, molecular representations, and data augmentation strategies. Notably, we found that SVM can match or even surpass the performance of state-of-the-art DL methods. However, conventional data augmentation often involves a trade-off between the true positive rate and false positive rate. To address this, we introduce Negative-Augmented PU-bagging (NAPU-bagging) SVM, a novel semi-supervised learning framework. By leveraging ensemble SVM classifiers trained on resampled bags containing positive, negative, and unlabeled data, our approach is capable of managing false positive rates while maintaining high recall rates. We applied this method to the identification of multitarget-directed ligands (MTDLs), where high recall rates are critical for compiling a list of interaction candidate compounds. Case studies demonstrate that NAPU-bagging SVM can identify structurally novel MTDL hits for ALK-EGFR with favorable docking scores and binding modes, as well as pan-agonists for dopamine receptors. The NAPU-bagging SVM methodology should serve as a promising avenue to virtual screening, especially for the discovery of MTDLs. [ABSTRACT FROM AUTHOR]

Irreversible perturbations in Langevin dynamics have been widely recognized for their role in accelerating convergence in simulations of multi-modal distributions π (θ) . A commonly used and easily computed standard irreversible perturbation is J ∇ log π (θ) , where J is a skew-symmetric matrix. However, Langevin dynamics employing a fixed-scale standard irreversible perturbation encounter a trade-off between local exploitation and global exploration, associated with small and large scales of standard irreversible perturbation, respectively. To address this trade-off, we introduce the adaptive irreversible perturbations Langevin dynamics, where the scale of the standard irreversible perturbation changes adaptively. Through numerical examples, we demonstrate that adaptive irreversible perturbations in Langevin dynamics can enhance performance compared to fixed-scale irreversible perturbations. [ABSTRACT FROM AUTHOR]

As a guide rail is the basic motion unit of precision equipment, the measurement of and compensation for its motion errors are important preconditions for precision machining and manufacturing. A targetless and simultaneous measurement method of three-degree-of-freedom (3-DOF) angular motion errors using digital speckle pattern interferometry (DSPI) is introduced in this paper. Based on the analysis of the sensitivity mechanism of DSPI to DOF errors and the formation mechanism of the phase fringes, the relationship between the angular motion errors and the distribution of the interferometric phases was established, and a new simultaneous measurement model of 3-DOF angular motion errors was further proposed. An optical setup based on a three-dimensional spatial-carrier DSPI with a right-angle symmetrical layout was used in the measurement system. Furthermore, repetitive tests, noise tests, and precision analysis were carried out to verify the performance of the system. The test results showed that the measurement resolution of the system was <1 μrad, which is capable of measuring the pitch angle, yaw angle, and roll angle at the submicron arc level simultaneously without target mirrors. The method has the advantages of no need to install cooperative targets and high measurement resolution, showing broad application prospects in many fields, including mechanical manufacturing, laser detection, aerospace, etc. [ABSTRACT FROM AUTHOR]

Leadless pacemakers with an atrioventricular synchrony algorithm represent a novel technology for patients qualified for VDD pacing. The current evidence of their performance is limited to several small-scale observational studies. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of this new technology. We systematically searched the PubMed, Embase, and Cochrane library databases from their inception to 12 September 2022. The primary efficacy outcome was atrioventricular synchrony after implantation, whereas the secondary efficacy outcome was the change in cardiac output represented by the left ventricular outflow tract velocity time integral (LVOT-VTI). The primary safety outcome was major complications related to the procedures and the algorithm. Means or mean differences with 95% confidence interval (95% CI) were combined using a random-effects model or a fixed-effects model. Finally, 8 published studies with 464 participants were included in the qualitative analysis. The pooled atrioventricular synchrony proportion was 78.9% (95% CI 71.9–86.0%), and a further meta-regression did not screen factors that contributed significantly to the heterogeneity. Additionally, a significant increase in atrioventricular synchrony of 11.3% (95% CI 7.0–15.7%, p < 0.01) was achieved in patients experiencing programming optimization. LVOT-VTI was significantly increased by 1.9 cm (95% CI 1.2–2.6, p < 0.01), compared with the VVI pacing mode. The overall incidence of complications was approximately 6.3%, with major complications related to the algorithm being extremely low. Overall, leadless pacemakers with atrioventricular synchronous pacing demonstrated favorable safety and efficacy. Future data on their long-term performance are required to facilitate their widespread adoption in clinical practice. [ABSTRACT FROM AUTHOR]

The nonlinear bending of the sandwich plates with graphene nanoplatelets (GPLs) reinforced porous composite (GNRPC) core and two metal skins subjected to different boundary conditions and various loads, such as the concentrated load at the center, linear loads with different slopes passing through the center, linear eccentric loads, uniform loads, and trapezoidal loads, has been presented. The popular four-unknown refined theory accounting for the thickness stretching effects has been employed to model the mechanics of the sandwich plates. The governing equations have been derived from the nonlinear Von Karman strain–displacement relationship and principle of virtual work with subsequent solution by employing the classical finite element method in combination with the Newton downhill method. The convergence of the numerical results has been checked. The accuracy and efficiency of the theory have been confirmed by comparing the obtained results with those available in the literature. Furthermore, a parametric study has been carried out to analyze the effects of load type, boundary conditions, porosity coefficient, GPLs weight fraction, GPLs geometry, and concentrated load radius on the nonlinear central bending deflections of the sandwich plates. In addition, the numerical results reveal that the adopted higher order theory can significantly improve the simulation of the transverse deflection in the thickness direction. [ABSTRACT FROM AUTHOR]

In many countries, governments have implemented non-pharmaceutical techniques to limit COVID-19 transmission. Restricting human mobility is one of the most common interventions, including lockdown, travel restrictions, working from home, etc. However, due to the strong transmission ability of the virus variants, further rounds of interventions, including a strict lockdown, are not considered as effective as expected. The paper aims to understand how the lockdown policy and pandemics changed human mobility in the real scenario. Here we focus on understanding the mobility changes caused by compliance with restrictions and risk perceptions, using a mobility index from the Google report during three strict lockdown periods in Leeds, the largest city in the county of West Yorkshire, England, from March 2020 to March 2021. The research uses time-varying z-scores and Principal Component Analysis (PCA) to simulate how local people dynamically process and perceive health risks based on multi-dimensional daily COVID-19 reports first. Further modelling highlights exponentially increasing policy non-compliance through the duration of lockdown, probably attributable to factors such as mental anxiety and economic pressures. Finally, the proposed nonlinear regression model examines the mobility changes caused by the population's dynamic risk perceptions and lockdown duration. The case study model in Leeds shows a good fit to the empirical mobility data and indicates that the third lockdown policy took effect much slower than the first. At the same time, the negative impact of the epidemic on population mobility decayed by 40% in the third lockdown period in contrast with the first lockdown. The risk perception estimation methods could reflect that the local population became increasingly accustomed to the COVID-19 situation, and local people rationally evaluated the risks of COVID in the third lockdown period. The results demonstrate that simulated risk perceptions and policy decay could explain urban mobility behaviour during lockdown periods, which could be a reference for future decision-making processes. [ABSTRACT FROM AUTHOR]

Since real experimental shearography images are usually few, the application of deep learning for defect detection in digital shearography is limited. A simulation dataset preparation method of shearography images is proposed in this paper. Firstly, deformation distributions are estimated by finite element analysis (FEA); secondly, phase maps are calculated according to the optical shearography system; finally, simulated shearography images are obtained after 2π modulus and gray transform. Various settings in the parameters of object, defect, load and shearing in those three steps could prepare a diverse simulation dataset for deep learning. Together with the real experimental images taken from a shearography setup, hybrid trainings of deep learning for defect detection are performed and discussed. The results show that a simulation dataset, generated without any real defective specimen, shearography system or manual experiment, can greatly improve the generalization of a deep learning network when the number of experimental training images is small. [ABSTRACT FROM AUTHOR]

Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific information. Moreover, over-fitting may occur when a short time window (TW) length was used in CCA. In this article, an optimized L1-regularized multiway canonical correlation analysis (L1-MCCA) is combined with a support vector machine (SVM) to overcome the aforementioned shortcomings in CCA. The correlation coefficients obtained by L1-MCCA were transferred into a particle-swarm-optimization (PSO)-optimized support vector machine (SVM) classifier to improve the classification accuracy. The performance of the proposed method was evaluated and compared with the traditional CCA and power spectral density (PSD) methods. The results showed that the accuracy of the L1-MCCA-PSO-SVM was 96.36% and 98.18% respectively when the TW lengths were 2 s and 6 s. This accuracy is higher than that of the traditional CCA and PSD methods. [ABSTRACT FROM AUTHOR]

Composite materials have seen widespread use in the aerospace industry and are becoming increasingly popular in the automotive industry due to their high strength and low weight characteristics. The increasing usage of composite materials has resulted in the need for more effective techniques for nondestructive testing (NDT) of composite structures. Of these techniques, digital shearography is one the most sensitive and accurate methods for NDT. Digital shearography can directly measure strain with high sensitivity when combined with different optical setups, phase-shift techniques, and algorithms. Its simple setup and less sensitivity to environmental disturbances make it particularly well suited for practical NDT applications. This paper provides a review of the phase measurement technique and recent developments in digital shearographic NDT. The introduction of new techniques has expanded the range of digital shearography applications and made it possible to measure larger fields and detect more directional or deeper defects. At the same time, shearography for different materials is also under research, including specular surface materials, metallic materials, etc. Through the discussion of recent developments, the future development trend of digital shearography is analyzed, and the potentials and limitations are demonstrated. [ABSTRACT FROM AUTHOR]

The hydrophobicity of a support plays a critical role in the catalytic efficiency of immobilized lipases. 3-aminopropyltriethoxysilane (APTES)-modified silica clay (A-SC) was coupled with silane coupling agents of different alkyl chains (methyl triethoxysilane, vinyl triethoxysilane, octyl triethoxysilane, and dodecyl triethoxysilane) to prepare a series of hydrophobic support for lipase immobilization. The lipases were immobilized onto the support by conducting glutaraldehyde cross-linking processes. The results showed that the activity of the immobilized biocatalyst increased with hydrophobicity. The hydrolytic activity of Lip-Glu-C12-SC (contact angle 119.8°) can reach 5900 U/g, which was about three times that of Lip-Glu-A-SC (contact angle 46.5°). The immobilized lipase was applied as a biocatalyst for biodiesel production. The results showed that the catalytic yield of biodiesel with highly hydrophobic Lip-Glu-C12-SC could be as high as 96%, which is about 30% higher than that of Lip-Glu-A-SC. After being recycled five times, the immobilized lipase still maintained good catalytic activity and stability. This study provides a good strategy to improve the efficiency of immobilized lipases, showing great potential for future industrial application on biodiesel production. [ABSTRACT FROM AUTHOR]

Docking profiles for (+)-strebloside, a cytotoxic cardiac glycoside identified from Streblus asper, and some of its derivatives and Na+/K+-ATPase have been investigated. In addition, binding between (+)-strebloside and its aglycone, strophanthidin, and several of their other molecular targets, including FIH-1, HDAC, KEAP1 and MDM2 (negative regulators of Nrf2 and p53, respectively), NF-κB, and PI3K and Akt1, have been inspected and compared with those for digoxin and its aglycone, digoxigenin. The results showed that (+)-strebloside, digoxin, and their aglycones bind to KEAP1 and MDM2, while (+)-strebloside, strophanthidin, and digoxigenin dock to the active pocket of PI3K, and (+)-strebloside and digoxin interact with FIH-1. Thus, these cardiac glycosides could directly target HIF-1, Nrf2, and p53 protein–protein interactions, Na+/K+-ATPase, and PI3K to mediate their antitumor activity. Overall, (+)-strebloside seems more promising than digoxin for the development of potential anticancer agents. [ABSTRACT FROM AUTHOR]

Digoxin is a cardiac glycoside long used to treat congestive heart failure and found recently to show antitumor potential. The hydroxy groups connected at the C-12, C-14, and C-3′a positions; the C-17 unsaturated lactone unit; the conformation of the steroid core; and the C-3 saccharide moiety have been demonstrated as being important for digoxin's cytotoxicity and interactions with Na+/K+-ATPase. The docking profiles for digoxin and several derivatives and Na+/K+-ATPase were investigated; an additional small Asn130 side pocket was revealed, which could be useful in the design of novel digoxin-like antitumor agents. In addition, the docking scores for digoxin and its derivatives were found to correlate with their cytotoxicity, indicating a potential use of these values in the prediction of the cancer cell cytotoxicity of other cardiac glycosides. Moreover, in these docking studies, digoxin was found to bind to FIH-1 and NF-κB but not HDAC, IAP, and PI3K, suggesting that this cardiac glycoside directly targets FIH-1, Na+/K+-ATPase, and NF-κB to mediate its antitumor potential. Differentially, digoxigenin, the aglycon of digoxin, binds to HDAC and PI3K, but not FIH-1, IAP, Na+/K+-ATPase, and NF-κB, indicating that this compound may target tumor autophagy and metabolism to mediate its antitumor propensity. [ABSTRACT FROM AUTHOR]

The overuse of insecticides with limited modes of action has led to resistance in mosquito vectors. Thus, insecticides with novel modes of action are needed. Secondary metabolites in Madagascan plants of the genus Cinnamosma (Canellaceae) are commonly used in traditional remedies and known to elicit antifeedant and toxic effects in insect pests. Here we test the hypothesis that extracts of Cinnamosma sp. enriched in drimane sesquiterpenes are toxic and/or antifeedant to the yellow fever mosquito Aedes aegypti. We show that the bark and root extracts, which contain a higher abundance of drimane sesquiterpenes compared to leaves, were the most efficacious. Screening isolated compounds revealed cinnamodial to be the primary driver of adulticidal activity, whereas cinnamodial, polygodial, cinnafragrin A, and capsicodendrin contributed to the larvicidal activity. Moreover, an abundant lactone (cinnamosmolide) in the root extract synergized the larvicidal effects of cinnamodial. The antifeedant activity of the extracts was primarily contributed to cinnamodial, polygodial, and cinnamolide. Parallel experiments with warburganal isolated from Warburgia ugandensis (Canellaceae) revealed that aldehydes are critical for—and a hydroxyl modulates—insecticidal activity. Our results indicate that plant drimane sesquiterpenes provide valuable chemical platforms for developing insecticides and repellents to control mosquito vectors. [ABSTRACT FROM AUTHOR]